Resumen
© 2017 IEEE. This work presents techniques for obtaining a reliable electrical load-curve based on comparative analysis between the different compressed sensing algorithms. Therefore, the goal is implementing compressed sensing (CS) when a wireless heterogeneous network, that exchanges information between electrical enterprise and smart meters, has a fault. Then, the data cannot be sent totally, and we would have the data only of some smart meters; thus, using the adequate technique of compressed sensing is possible to the reconstruction of load-curve required for generating demand response (DR) with the minimum error. In the advanced metering infrastructure (AMI) there may be communication faults; then, it is necessary to have other forms for estimating the demand response using few measurements. In addition, using a dictionary based on the DCT transform does not mean that the sea is the best option for the representation of a signal. For example, among other results, in this work we obtain an average of percent root mean square difference nearest to the 5% in relation with a Gaussian function or Wavelet basis with values between 1.4 and 1.7% average PRD.
Título traducido de la contribución | Reconstrucción de la curva de carga eléctrica requerida para la respuesta a la demanda utilizando técnicas de detección comprimidas |
---|---|
Idioma original | Inglés estadounidense |
Páginas | 1-6 |
Número de páginas | 6 |
DOI | |
Estado | Publicada - 1 dic. 2017 |
Evento | 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017 - Quito, Ecuador Duración: 20 sep. 2017 → 22 sep. 2017 |
Conferencia
Conferencia | 2017 IEEE PES Innovative Smart Grid Technologies Conference - Latin America, ISGT Latin America 2017 |
---|---|
Título abreviado | ISGT Latin America 2017 |
País/Territorio | Ecuador |
Ciudad | Quito |
Período | 20/09/17 → 22/09/17 |